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Intelligent Scheduling Method for Satellite Ground Station Resources

机译:卫星地面站资源的智能调度方法

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China Remote Sensing Satellite Ground Station (RSGS), whose satellite-ground communication missions include telemetry, tracking and command(TT&C) and data transmission (DT ) missions, has a ground station network of 5 ground stations and serves more than 30 satellites. In recent years, the number of satellite-ground communication missions has increased sharply and this fact results in that resources of ground stations became relatively insufficient, which makes the resource scheduling significant. In this paper an intelligent ground station resource scheduling strategy is provided to solve the large-scale mix integer problem with individual needs of operators and resource usage considered. The common individual needs and resource usage are operating habits, preferences of operators, resource availability for data of different types, reliability and load balancing of multiple resources, etc. A hybrid decomposition algorithm is proposed in this paper: firstly, the overall problem is decomposed into multiple small sub-problems after a pre-process by a discrete particle swarm optimization (DPSO) algorithm; secondly, sub-problems are grouped by their solving complexity and the heuristic method or the mixed-integer linear programming (MILP) method is chosen adaptively to solve sub-problems. The method is applied to the actual operation system of RSGS and the rationality of the model and the effectiveness of the algorithm are proved in real cases.
机译:中国遥感卫星地面站(RSGS)的卫星对地面通信任务包括遥测,跟踪与指挥(TT&C)和数据传输(DT)任务,它具有由5个地面站组成的地面站网络,为30多颗卫星提供服务。近年来,卫星-地面通信任务的数量急剧增加,并且这一事实导致地面站的资源变得相对不足,这使得资源调度变得重要。本文提出了一种智能地面站资源调度策略,以解决运营商的个性化需求和资源使用情况的大规模混合整数问题。常见的个人需求和资源使用情况包括操作习惯,操作员的偏好,不同类型数据的资源可用性,多种资源的可靠性和负载均衡等。本文提出了一种混合分解算法:首先,对整个问题进行分解通过离散粒子群优化(DPSO)算法进行预处理后,分解为多个小子问题;其次,根据子问题的求解复杂度将子问题分组,并自适应地选择启发式方法或混合整数线性规划(MILP)方法来求解子问题。将该方法应用于RSGS的实际操作系统,在实际情况下证明了模型的合理性和算法的有效性。

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